Sr. Full Stack Data Science Engineer

Work Location: Toronto, Ontario, Canada

Hours 37.5

Line Of Business Analytics, Insights, & Artificial Intelligence

Pay Details $154,000 - $199,500 CAD

The pay details posted reflect a temporary market premium specific to this role that is reassessed annually.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate’s skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description Department Overview Join a high-impact analytics team that shapes business decisions through data, insights, and AI/ML. Collaborate with business leaders and cross-functional teams to uncover opportunities, build scalable analytics solutions, and translate complex analysis into actionable insights.

Key Responsibilities

• Lead end-to-end performance diagnostics across customer, product, and advisor dimensions to identify growth, efficiency, and primacy opportunities.
• Translate curated data into actionable insights through hypothesis development, testing, analysis, and stakeholder storytelling.
• Design and deliver scalable analytics assets, including datasets, dashboards, segmentation frameworks, and predictive AI/ML models.
• Investigate, evaluate, and implement AI/ML tools and algorithms to solve complex business problems.
• Develop compelling visualizations and data stories tailored to technical and non-technical audiences.
• Partner with business owners to drive advanced analytics and AI/ML adoption.
• Lead cross-functional collaboration with data scientists, engineers, IT partners, and business process owners.
• Provide subject-matter expertise, mentorship, and guidance on advanced analytics and AI/ML methodologies.
• Identify emerging analytical trends and data needs to improve repeatable and scalable solutions.

Required Qualifications & Skills

• Business Acumen: Strong ability to frame and structure complex business problems in financial services / retail banking, connect analytical insights to commercial levers (growth, efficiency, customer and advisor outcomes), and translate findings into clear, actionable recommendations. Demonstrated comfort engaging with senior executives and C‑suite stakeholders, influencing decisions through concise, insight‑driven storytelling.
• Applied Analytics Expertise: Demonstrated ability to creatively explore data, identify non‑obvious patterns, and rigorously test hypotheses to solve complex business problems. Brings an entrepreneurial mindset to analytics by proactively identifying opportunities, challenging assumptions, and delivering high‑impact insights that drive informed decision‑making.
• ML/AI Lifecycle Familiarity: Experience working with existing ML/AI models (adjusting inputs, interpreting outputs) and building or modifying models as needed. Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models
• Solid cloud experience with Azure or AWS and cloud AI/ML services such as Databricks, Kubernetes, docker and container orchestration, Azure Machine Learning, Azure Data Factory
• Visualization & Communication: Proficient in creating clear, compelling dashboards, visualizations, and data stories tailored to diverse audiences, including senior executives and C‑suite leaders, translating complex analysis into concise, decision‑ready narratives.
• Data Stewardship: Confident working with structured and unstructured data from multiple sources, ensuring data usability, cleanliness, and reliability. Able to build or modify data pipelines or analytical assets.
• Core Analytical Tools: Proficient in Python, PySpark, SQL, Power BI, and Databricks (or similar platforms) for data preparation, analysis, and collaboration.
• Strong experience with PySpark for big data processing and PyTorch for deep learning model serving.
• Non-Technical Skills: Strong relationship management, storytelling, and busine…